{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Music auto-tagging, classification, and embedding extraction\n",
    "In this tutorial, we use Essentia's TensorFlow integration to perform auto-tagging, classification, and embedding extraction.\n",
    "\n",
    "\n",
    "## Setup\n",
    "First of all, notice that the default Essentia's `pip` package does not include TensorFlow support.\n",
    "Currently, this is only available through `pip` for Linux with Python `>=3.5` and `<=3.7`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q essentia-tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can follow the instructions in this [blog post](https://mtg.github.io/essentia-labs/news/tensorflow/2019/10/19/tensorflow-models-in-essentia/) to build Essentia with TensorFlow support. This approach also works in Mac, for Python `>=3.8` or with TensorFlow `>=2.0`.\n",
    "\n",
    "\n",
    "After this step, we can import the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "from essentia.standard import MonoLoader, TensorflowPredictMusiCNN, TensorflowPredictVGGish\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Essentia models](https://essentia.upf.edu/models.html)' site contains models for diverse purposes.\n",
    "Each model comes with a `.json` metadata file with information such as the classes it can predict, the metrics achieved on training, or the model's version.\n",
    "\n",
    "Let's download the `MusiCNN` auto-tagging model trained on the Million Song Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -q https://essentia.upf.edu/models/autotagging/msd/msd-musicnn-1.pb\n",
    "!wget -q https://essentia.upf.edu/models/autotagging/msd/msd-musicnn-1.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can open the metadata file and check all the available keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['name', 'type', 'link', 'version', 'description', 'author', 'email', 'release_date', 'framework', 'framework_version', 'classes', 'model_types', 'dataset', 'schema', 'citation'])\n"
     ]
    }
   ],
   "source": [
    "with open('msd-musicnn-1.json', 'r') as json_file:\n",
    "    metadata = json.load(json_file)\n",
    "\n",
    "print(metadata.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we load an audio file to use as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_file = '../../../test/audio/recorded/techno_loop.wav'\n",
    "audio = MonoLoader(sampleRate=16000, filename=audio_file)()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto-tagging\n",
    "Now we have all the ingredients to perform auto-tagging with `MusiCNN`.\n",
    "`TensorflowPredictMusiCNN` is our dedicated algorithm to make predictions with models expecting `MusiCNN`'s mel-spectrogram signature.\n",
    "We have to configure the `graphFilename` parameter with the path to the model and feed the algorithm with audio sampled at 16kHz. \n",
    "The output is a two-dimensional matrix `[time, activations]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "activations = TensorflowPredictMusiCNN(graphFilename='msd-musicnn-1.pb')(audio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we use `matplotlib` to plot the activations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "ig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "ax.matshow(activations.T, aspect='auto')\n",
    "\n",
    "ax.set_yticks(range(len(metadata['classes'])))\n",
    "ax.set_yticklabels(metadata['classes'])\n",
    "ax.set_xlabel('patch number')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "plt.title('Tag activations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification with the transfer learning classifiers\n",
    "Essentia is shipped with a collection of single-label classifiers based on transfer learning that rely on our auto-tagging models or other deep embedding extractors.\n",
    "You can read our [blog post](https://mtg.github.io/essentia-labs/news/tensorflow/2020/01/16/tensorflow-models-released/) for a complete list of the available classifiers.\n",
    "\n",
    "In this example, we use the `danceability` classifier based on the `VGGish` model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -q https://essentia.upf.edu/models/classifiers/danceability/danceability-vggish-audioset-1.pb\n",
    "!wget -q https://essentia.upf.edu/models/classifiers/danceability/danceability-vggish-audioset-1.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the `TensorflowPredictVGGish` algorithm because it generates the required mel-spectrogram signature for this case, but if we wanted to use the `danceability` classifier based on `MusiCNN` we should use `TensorflowPredictMusiCNN` again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('danceability-vggish-audioset-1.json', 'r') as json_file:\n",
    "    metadata = json.load(json_file)\n",
    "\n",
    "activations = TensorflowPredictVGGish(graphFilename='danceability-vggish-audioset-1.pb')(audio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can compute the global accuracy as the mean of the activations along the temporal axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "danceable: 99.8%\n",
      "not_danceable: 0.2%\n"
     ]
    }
   ],
   "source": [
    "for label, probability in zip(metadata['classes'], activations.mean(axis=0)):\n",
    "    print(f'{label}: {100 * probability:.1f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embedding extraction\n",
    "A usual transfer learning approach consists of extracting low-dimensional feature maps from one of the last layers of a pre-trained model as embeddings for a new downstream task.\n",
    "The metadata includes a field `schema` with the names of the most relevent layers of each model, including layers that are suitable for embedding extraction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'inputs': [{'name': 'model/Placeholder', 'type': 'float', 'shape': [187, 96]}], 'outputs': [{'name': 'model/Sigmoid', 'type': 'float', 'shape': [1, 50], 'op': 'Sigmoid'}, {'name': 'model/dense_1/BiasAdd', 'type': 'float', 'shape': [1, 50], 'op': 'fully connected', 'description': 'logits'}, {'name': 'model/dense/BiasAdd', 'type': 'float', 'shape': [1, 200], 'op': 'fully connected', 'description': 'embeddings'}]}\n"
     ]
    }
   ],
   "source": [
    "with open('msd-musicnn-1.json', 'r') as json_file:\n",
    "    metadata = json.load(json_file)\n",
    "\n",
    "print(metadata['schema'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this, we see that `model/dense_1/BiasAdd` is the recommended layer for embeddings.\n",
    "Additionally, an external tool such as [Netron](https://netron.app/) can be used to inspect the model and get the information about the whole architecture.\n",
    "\n",
    "`TensorflowPredictMusiCNN` offers an `output` parameter that can be configured to select the layer of the model to retrieve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = TensorflowPredictMusiCNN(graphFilename='msd-musicnn-1.pb', output='model/dense_1/BiasAdd')(audio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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kUAAAACWFVmRNPT6s2/7qWZEhNbM5NNxP1KZz4kbrDmWPIE5Wo7xqwrGU0YB2ZlN8TCmngWdKI9gkvYTPmfQSPo9Qm4yPKUndRnzM+nh8TEnqjOTEnQ8zUAAAACWRQAEAAJS0aAJlZmeY2Y1mdoeZ3W5mby3u32pmN5jZPcX/W47/cAEAAPIt5ep0R9K/c/fvmtmYpO+Y2Q2S3iTpK+5+uZldJukySW9f6IkqHWlkX2yDvuaB+IZqkpTQh1CdRvy+Zi0q2TgSX0TSGk1qlDcVv6+d4YTFhKfCQ0qSRvfEF3xNb4lvLpklo3bQEs6/GXVXUk69YkIfWElSr5YUeB6LniXdfY+7f7e4fUTSnZJOk3SBpKuLh10t6cLjNEYAAICBUurPTDM7S9LPSbpJ0g5331Ns2itpx+oODQAAYDAtOYEys1FJn5L0b9398Nxt7u7SsRfsMrNLzWyXme1qzyR99hEAAGAVLSmBMrO6+snTNe7+6eLuR83slGL7KZL2Het73f0Kd9/p7jvrjdHVGDMAAECqRYvIzcwkfUTSne7+X+dsul7SxZIuL/6/brHnao+5HnlJbEVhZUPOCuS9TkLBcUJMG0qo1lxnfCqhE2FCZao1co6ljM+ZeCuhsrqXU4BrzfifqyVUrvfaSR8MyKjSbyV9eqiesK8fnX/TUs7ML5T0Rkm3mdmtxX1/qH7i9Akzu0TSg5Jeu6JBAgAArBGLJlDu/g1J8/3p8pLVHQ4AAMDgoxM5AABASaHFFZVp08a7Yus5Kp2E+hFpns8kHl8Zi3bKkrrHZSzsm9TDrTYVv7Od4YzCoJxjKaMRYcqxlLQYdm8o/udqGc0l19N0RNK5sNKOj/njBbatpx85AADAqiCBAgAAKIkECgAAoKTQqhmvSq1NkRGlWtICpRnXatsj8TGzUvD1VOPQbcYXHOTU0yXElFSbjI+ZcSx1G/ExJaXUXqUssJt1Lsyot81aODnh9+pCmIECAAAoiQQKAACgJBIoAACAkkigAAAASgotFR061NWZ1x+KDKnu6FBovFlDDz0eHrO7bSw8pio5ObjNxFcTdkdzqnBr+8fDY/Y2NMNjpr1Xdz8RHrOzfWN4zOrh6fCYkuQJ54jOluHwmEMPHwiPKUm9jfGfHrKJnGOpNxa/r3cusI0ZKAAAgJJIoAAAAEoigQIAACgptAbKuj1VxmM7W5rnrKDpG+KvwVs3fl+7jZwcvDbeDY9pnV54TElSPb6rZcr7JuvPuan4eo7KVPz5wYdyFla3VnxXy8pMfMzOyfF1bVL/92p4zIQFoiXJ64M15zNYowEAAFgDSKAAAABKIoECAAAoKXYx4XpV7R2x14mr0wmrSkrqNePrDdob4/voZNVAVabif67dkZzr/p2x+J9rZ7gaHjNLfWvwCueS2gl9irrNnJ9p/XArJW601pacPma1yfh6UN8U3ydOyvt9M5/BGg0AAMAaQAIFAABQEgkUAABASSRQAAAAJcUWkVdN7Y2xhbgz23IK+0Z+PBEeM6PALqN5pyS1Tk4owh3K+XujPhFfMJ/xc22P5RQ5t7fGL1DaGYnfV0vqA9valHAOTnirNvflLLCb8eGhXs3CY0qSJ8WdDzNQAAAAJZFAAQAAlEQCBQAAUFJoDVRrk+nH58fmbJWZnGum1olfWLLbjK9bsZwSKHk1IXDSvtYmGuExO6MJC5Tm9LyVdeKbAmbUI/UaOQdwrxG/s9WJhHrQ3mh4TCnnXNgdySmoqx1KqJO8fv5NzEABAACURAIFAABQEgkUAABASaE1UNVJaestsTlbL2f9V9USWoJ0huPrvbyaU2NWP5LQp2hD0r5OJOzraHytQS9+/W1JUvOJjGMpPKQqnaQeOr2EupWEqQFPmo6wbsbPNadnW7dBHygAAIA1jQQKAACgJBIoAACAkhZNoMysaWbfNrPvmdntZvau4v6zzewmM7vXzD5uZjmLzgEAAARbStnmjKQXu/u4mdUlfcPM/o+kt0l6t7tfa2YflHSJpA8s9ES9ujR5amwRWHUqNNxP9BLSydZYfMxKUvPD1thgFRMeT61N8fua8eGLSjs+piRNW8Lrm1Aw30koXJekSis+ZkaDX+vGx5QkT6jnzvpwVtY5Yj6LzkB533jxZb3455JeLOmTxf1XS7rweAwQAABg0CypBsrMqmZ2q6R9km6Q9CNJB919dv7hYUmnzfO9l5rZLjPb1Z2cWIUhAwAA5FpSAuXuXXc/T9Lpkp4v6dylBnD3K9x9p7vvrI4kzSEDAACsolJX4t39oJndKOkFkjabWa2YhTpd0u5Fg01LW+6KvVDcGsv5oKEnlOgMPxZ/4b/TzKlFqrbi97VXT2qkOZmwrxk1OknHUsbCvhnnh8b9OQvAZjT47SU0+M2oRZKk6kz8+SGr9jXrHDGfpXwK72Qz21zcHpb0Mkl3SrpR0quLh10s6brjNEYAAICBspS/M0+RdLWZVdVPuD7h7p81szskXWtmfyrpFkkfOY7jBAAAGBiLJlDu/n1JP3eM++9Tvx4KAABgXaETOQAAQEmhpaLWc9UnYwsZG4dyuptVZ+Ljtsbiu5vVppMKq4/EVzH2Gjl/b2Q06MtY9TzrWGociO/O16vF72t3OKfKuTYdX7xeTYjZTTo/VFoJ+5p2LKWEnRczUAAAACWRQAEAAJREAgUAAFBSaA1Ua5Ppx+fH5mzWTmrO143vRNgdSWiUV8lpzqduwjX4asIKpZIqk/H72mvG/1yzFmOtTDXCY/aGE17fTs65sNdIOEdU4+cGrJXUtDmhns5mkupBM07BfzP/JmagAAAASiKBAgAAKIkECgAAoKTQQp2hQ64zvhB7EbPTDA33E9V2/MVar8RfC+8k9T6J7icmSd2hnH2ttjPqZcJDamZzTo3OyL74PlCtjfF1bZWE40iSPGFh34z3asYC55LUacbva/PxnNWEOyPx75sHFtjGDBQAAEBJJFAAAAAlkUABAACURAIFAABQUmgReWfYtP9Z8Q0mU2TUwybUMKY0NpO0nnL/XsJbphJfV53znpF05Iyh+KDr5Pwg5ZwjPOlYSpGwrynvGUk+YKf9ARsOAADA4COBAgAAKIkECgAAoKTQ6or64Z7O+PKRyJBqbYlfKFSSehkLPCb0yfOENX0lqbl3Mjzm1KkbwmNK0vDeqfCY7Y3xNQ6tTTn1kaM/Ohwec+r00fCYtcmc1ZorrfgTUy+hkWZrY87x23i8FR80oTmqJLU21lPizocZKAAAgJJIoAAAAEoigQIAACgp9qJt1dQZHaxrmMdLpZPQ/KSXsYBxTg7eGYuvbbOMn6mkXi3+Nc7oo1Odzlnstr1lODxmJWGx8YxaJEnqNpMKJYNVkhYT7g3Fv76W8LtGkqpTOXV882EGCgAAoCQSKAAAgJJIoAAAAEoigQIAACgpfDHhx5/ZjAypTnx9aF9CjV2lEx8za3FH68V/GCGraWh1Jn5fewn76mnrjCccSwlF+rXJnAVgE96qicdSvIwGyimLjUvqZhzCN8y/iRkoAACAkkigAAAASiKBAgAAKCn0SnFtoqeTvzsRGVLdZs7FcPP4IqisRo8ZMhq5ecIC0VkyjqWMBWClnAaTGc0lKzM5TQgz3qu9RkJzyazzb8Lbxto5TVkzmobevsA2ZqAAAABKIoECAAAoackJlJlVzewWM/ts8fXZZnaTmd1rZh83s5zPyAIAAAQrUyD0Vkl3StpYfP1nkt7t7tea2QclXSLpAws9wcy2iu69KLYPlHIu1cqr66QeqZ6zn5Xx+GvhvUbSwZRQemXd+KBeSzqWpuIn4nvN+GPJ2knNkRKO35Tzb9K5MEPtQM6x1B1JOAffOP+mJZ05zOx0Sf9E0v8ovjZJL5b0yeIhV0u6cAVDBAAAWDOW+qfXeyT9e/39fM42SQfdfbb39cOSTjvWN5rZpWa2y8x2dcfHVzJWAACAgbBoAmVmr5K0z92/s5wA7n6Fu+90953V0dHlPAUAAMBAWcqFzBdK+mdmdr6kpvo1UH8habOZ1YpZqNMl7T5+wwQAABgciyZQ7v4Hkv5AkszslyX9nrtfZGb/S9KrJV0r6WJJ1y32XMPDM3rOsx5YwXDLO9JuhMab1e3FF6ZubEyHxzw4nbNa8+jQTHjMqU7CqqiSTh6Ov/Q92Yn/UG3W61vLWI01wabGVErcx6birzw0qvErq7czVuCWNFxLWtl3nXhwgW0r+S3/dklvM7N71a+J+sgKngsAAGDNKPVZRHf/qqSvFrfvk/T81R8SAADAYKMTOQAAQEmh3bD8gapab469Ht6o5VyX7t7xw/CYnac+JTzmWCdngVKvxNe2DQ/l1OiM7zkcH3RbcMNbSfVNI+ExJam6e3980Gb88TvhOa/vcCOh6aLFv1eHHnokPKYk+aaNiz9otTVyFh7xelIz2HkwAwUAAFASCRQAAEBJJFAAAAAlhV5QbI/VtO+XtkeGVCW+HYgkqfnU+A8oTm6Lr/fKen0rnfiFO3v1hFVRJVU6W8Nj9mrx+9rNadmmkTPj+xS1RuP/dvWkP5c94W2TcV6qP3tbfFBJ7ZH1cywpY73mBcqZmYECAAAoiQQKAACgJBIoAACAkkigAAAASgotIq9PdHXyrkORIdVr5jTeqkzFL/A4vDe+eVyvkdOotHq4FR6zuzGneVx1Mr4itleP/9uqM5rTqHT43vhGmp3t8c0PvZL0IYiZ+OO3OxL/XrVu0qLUFv9zrbRyGih3k37fzIcZKAAAgJJIoAAAAEoigQIAACgptEBoZktV9//apsiQam1Nui7tw+EhqxPx+XB3Q87rW52IX+w2a1/rB+P31RNKB7vDGV3yJK/vSAgaH7K5N6d+pD2a0PS2GR/TaznHr7Xia6CGDubMvbTHEl7j/zf/JmagAAAASiKBAgAAKIkECgAAoCQSKAAAgJJCS0WHjrhO/78zkSHVGUlq9DgTX3Cc0fyw0s4prLZefDFhdyjn7w3rxjci9Gp8YWov6fVtHIhvytraHN80tNLOaX5YnYmP294Q/ymIjHO+JPVq8e9V6+YUzHc2xP8+v3+BbcxAAQAAlEQCBQAAUBIJFAAAQEmhF4ptuq3GvY9GhtRQM2cBWJuYig/abMTHTFjIUpLUja+r8KGcxW71ROwC3JJkY6PhMb2es/B3xnu13kg4L83E13pJkmrxdStDlYS5gXZ8raKknHNw0sLUyvi5LmCwRgMAALAGkEABAACURAIFAABQUmjRwbanjeuNn/7byJDqJuWIY5X4uorJXnwNVNbr2/b4uoqKcvq8bK8dCY+5rzMWHrNqOb1lmtYOj1m3+HqZjPODJE0kxG1W4n+mGef8LI+0t6TE3VydDI/5hafOv40ZKAAAgJJIoAAAAEoigQIAACiJBAoAAKCk0CLyx+8e1f988QsiQ0pZzQ89pyA2XFYjzanp+JgZjUqldXMsradGmhpuxsdsxRdWS5I6CQ0mMxqVVnMWrs9oKpx1LPmG4YSoN8+7hRkoAACAkkigAAAASiKBAgAAKMk8sL7CzB6T9OAyv/0kSftXcThYnziOsFo4lrBaOJYG15nufvKxNoQmUCthZrvcfWf2OLC2cRxhtXAsYbVwLK1NXMIDAAAoiQQKAACgpLWUQF2RPQCcEDiOsFo4lrBaOJbWoDVTAwUAADAo1tIMFAAAwEAggQIAAChp4BMoM3uFmd1tZvea2WXZ48HaZWYPmNltZnarme3KHg/WDjO70sz2mdkP5ty31cxuMLN7iv+3ZI4Ra8M8x9I7zWx3cW661czOzxwjlmagEygzq0p6n6RXSnqGpDeY2TNyR4U17lfc/Tx6rqCkqyS94qj7LpP0FXc/R9JXiq+BxVylf3gsSdK7i3PTee7++eAxYRkGOoGS9HxJ97r7fe7eknStpAuSxwRgnXH3r0s6cNTdF0i6urh9taQLI8eEtWmeYwlr0KAnUKdJemjO1w8X9wHL4ZK+ZGbfMbNLsweDNW+Hu+8pbu+VtCNzMFjz3mJm3y8u8XE5eA0Y9AQKWE0vcvfnqn9J+HfM7JeyB4QTg/f7wdATBsv1AUlPkXSepD2S/jx1NFiSQU+gdks6Y87Xpxf3AaW5++7i/32SPqP+JWJguR41s1Mkqfh/X/J4sEa5+6Pu3nX3nqQPi3PTmjDoCdTNks4xs7PNbEjS6yVdnzwmrEFmtsHMxmZvS3q5pB8s/F3Agq6XdHFx+2JJ1yWOBWvYbCJe+FVxbloTatkDWIi7d8zsLZK+KKkq6Up3vz15WFibdkj6jJlJ/eP+Y+7+hdwhYa0ws7+W9MuSTjKzhyX9kaTLJX3CzC6R9KCk1+aNEGvFPMfSL5vZeepfBn5A0r/KGh+WjqVcAAAAShr0S3gAAAADhwQKAACgJBIoAACAkkigAAAASiKBAgAAKIkECkAYM3uTmZ26hMe8N2pMx4j/TjP7vaz4ANYGEigAkd4kacEEai2zPs6rwDrAGx3AspjZWWZ2l5ldY2Z3mtknzWyk2PYOM7vZzH5gZlcUicWrJe2UdI2Z3Wpmw2b2PDP7WzP7npl9e7ZbvKRTzewLZnaPmf3neeI/YGbvMrPvmtltZnZucf+TZpCKMZw1Z7xXmdkPi3G/1My+WcSZu3zGz5rZt4r73zznuX6/2K/vm9m75rwOd5vZR9XvID13+SkAJygSKAAr8TRJ73f3p0s6LOlfF/e/192f5+7PkjQs6VXu/klJuyRd5O7nSepK+rikt7r7z0p6qaSp4vvPk/Q6Sc+W9Dozmy8p2V8sEP0BSUu57PYz6i/Uem7x79clvaj43j+c87jnSHqxpBdIeoeZnWpmL5d0jvrrlJ0n6efnLEh9TvE6PNPdH1zCOACscSRQAFbiIXf/ZnH7r9RPRiTpV8zsJjO7Tf1E5JnH+N6nSdrj7jdLkrsfdvdOse0r7n7I3acl3SHpzHnif7r4/zuSzlrCeO9399uKRVtvL+K4pNuO+v7r3H3K3fdLulH9pOnlxb9bJH1X/QTsnOLxD7r73y0hPoATxECvhQdg4B29FpSbWVPS+yXtdPeHzOydkpoln3dmzu2u5j9XzRzjMR09+Y/D5jEeL0m9OV/3jorxD/ZLkkn6T+7+obkbzOwsSRPzjA/ACYoZKAAr8dNm9oLi9q9L+ob+PmHZb2ajkl495/FHJM3WOd0t6RQze54kmdmYma3GH3UPSHpu8ZzPlXT2Mp7jAjNrmtk29Rd+vVn9Rc1/s9gnmdlpZrZ9FcYLYA1iBgrAStwt6XfM7Er1L7V9wN0nzezD6hdU71U/+Zh1laQPmtmU+vVFr5P0381sWP36p5euwpg+JelfmNntkm6S9MNlPMf31b90d5KkP3H3RyQ9YmZPl/QtM5OkcUm/of7sF4B1xvqX/wGgnOLS1WeLQnEAWFe4hAcAAFASM1AAAAAlMQMFAABQEgkUAABASSRQAAAAJZFAAQAAlEQCBQAAUNL/B9CxE52RfQXPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ig, ax = plt.subplots(1, 1, figsize=(10, 4))\n",
    "ax.matshow(embeddings.T, aspect='auto')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.set_xlabel('patch number')\n",
    "plt.title('Embeddings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "metadata": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
